Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation

ACL ARR 2025 February Submission4032 Authors

Abstract:

Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid '5Ws' questions. However, significant challenges remain when addressing '1H' questions, specifically how-to questions, which are integral for decision-making and require dynamic, step-by-step responses.
The key limitation lies in the prevalent data organization paradigm, chunk, which commonly divides documents into fixed-size segments, and disrupts the logical coherence and connections within the context. To address this, we propose THREAD, a novel data organization paradigm enabling systems to handle how-to questions more effectively. Specifically, we introduce a new knowledge granularity, 'logic unit' (LU), where large language models transform documents into more structured and loosely interconnected LUs.
Extensive experiments across both open-domain and industrial settings show that THREAD outperforms existing paradigms significantly, improving the success rate of handling how-to questions by 21% to 33%.
Additionally, THREAD demonstrates high adaptability across diverse document formats, reducing retrieval information by up to 75% compared to chunk, and also shows better generalizability to '5Ws' questions, such as multi-hop questions, outperforming other paradigms.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: Retrieval-augmented Generation, Data organization paradigm, How-to Questions, Large language models

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 4032